Population-level analysis of gut microbiome variation

Falony, Gwen; Joossens, Marie; Vieira-Silva, Sara; Wang, Jun; Darzi, Youssef; Faust, Karoline; Kurilshikov, Alexander; Bonder, Marc Jan; Valles‐Colomer, Mireia; Vandeputte, Doris; Tito, Raúl Y.; Chaffron, Samuel; Rymenans, Leen; Verspecht, Chloë; Sutter, Lise De; Lima‐Mendez, Gipsi; D’hoe, Kevin; Jonckheere, Karl; Homola, D; Garcia, Roberto; Tigchelaar, Ettje F.; Eeckhaudt, Linda; Fu, Jingyuan; Henckaerts, Liesbet; Zhernakova, Alexandra; Wijmenga, Cisca

doi:10.1126/science.aad3503

Cited by 1,889 publications

(1,812 citation statements)

References 54 publications

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“…In agreement with our predictions (Fig. 5), recent epidemiological studies have shown that BSS is the major explanatory variable for microbiota composition in healthy humans (43,44): Higher BSS scores (less colonic water uptake) correspond to a higher relative abundance of Bacteroidetes.…”

Section: Significancesupporting

confidence: 80%

“…6, Inset). Furthermore, microbiome sequencing has confirmed nutrient inflow, transit time, and water content to be crucial determinants of microbiota composition (31,32,45,46), with stool consistency (BSS) identified as the single most important factor explaining microbiota variation in a large-scale epidemiological study (44). Our results offer a minimal explanation for these variations: Nutrient inflow and stool consistency affect microbiota composition via changes in luminal pH.…”

Section: Discussionmentioning

confidence: 77%

“…The observed variation in phyla can be readily accounted for by differences in nutrient intake and stool consistency. Corroborating this observation, BSS has been identified as the single most important determinant for microbiota composition among 69 covariates studied (44). Parameters are as in SI Appendix, Tables S1 and S2.…”

Section: Methodsmentioning

confidence: 99%

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Effect of water flow and chemical environment on microbiota growth and composition in the human colon

Cremer

Arnoldini

Hwa

2017

Proc. Natl. Acad. Sci. U.S.A.

143

192

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The human gut harbors a dynamic microbial community whose composition bears great importance for the health of the host. Here, we investigate how colonic physiology impacts bacterial growth, which ultimately dictates microbiota composition. Combining measurements of bacterial physiology with analysis of published data on human physiology into a quantitative, comprehensive modeling framework, we show how water flow in the colon, in concert with other physiological factors, determine the abundances of the major bacterial phyla. Mechanistically, our model shows that local pH values in the lumen, which differentially affect the growth of different bacteria, drive changes in microbiota composition. It identifies key factors influencing the delicate regulation of colonic pH, including epithelial water absorption, nutrient inflow, and luminal buffering capacity, and generates testable predictions on their effects. Our findings show that a predictive and mechanistic understanding of microbial ecology in the gut is possible. Such predictive understanding is needed for the rational design of intervention strategies to actively control the microbiota. T he human gut microbiota is composed of trillions of bacterial cells (1-4) from several hundred species (1-3, 5, 6). Over the last two decades, a multitude of studies have shown a strong impact of human health status on the composition of this microbiota, and in turn a strong effect of microbiota composition on host physiology has also been confirmed (7-9). Various intervention strategies are being investigated to modify the microbiota composition via prebiotics and probiotics (10). Despite this importance for human health, little is known about how the microbiota composition is shaped by the interplay between human and bacterial physiology in the gut.In this study, we present a physiological model that quantitatively describes this host-microbiota interplay. Our model is based on a hydrodynamic perspective, which posits that bacterial densities reached in the colon result from a dynamic balance between bacterial growth, flow through the colon, and peristaltic mixing (11). To build the present model, we extensively analyzed literature data on human gut physiology to obtain quantitative estimates for a range of relevant host parameters. We further characterized the rates of bacterial growth, carbohydrate consumption, and fermentation product excretion for representatives of the two dominant bacteria phyla, Bacteroidetes and Firmicutes, that typically make up more than 90% of the bacterial cells observed in the gut (6).Combining these aspects of human and bacterial physiology into a coarse-grained mathematical model allowed us to study bacterial growth dynamics in the human gut. Without resorting to ad hoc fitting parameters, model results are in quantitative agreement with available data on key observables of human gut physiology. We find that changes in pH values in the colon that are due to the secretion of acidic fermentation products and shaped by human physiology (such a...

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Section: Significancesupporting

confidence: 80%

Section: Discussionmentioning

confidence: 77%

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Effect of water flow and chemical environment on microbiota growth and composition in the human colon

Cremer

Arnoldini

Hwa

2017

Proc. Natl. Acad. Sci. U.S.A.

143

192

View full text Add to dashboard Cite

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“…Recent studies employing these technologies have uncovered evidence for both environmental and host genetic association with the microbiome composition [8,11,20,[22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37]. However, to date there is no consensus regarding how and to what extent does host genetics shapes the gut microbiome, as compared to environmental factors.…”

Section: Introductionmentioning

confidence: 95%

“…First, there is an excessive bacterial similarity among individuals sharing a household, but no such similarity was observed across family members without a history of household sharing [8,20,22,35]. Second, over 20% of gut microbiome β-diversity variance can be inferred via several measured environmental factors, such as answers to food frequency and drug use questionnaires [8,33,34], whereas no statistically significant result was obtained when applying a similar methodology to genetic variants [8]. These results further demonstrate that the gut microbiome is predominantly shaped by environmental factors.…”

Section: The Microbiome Is Predominantly Shaped By Non-genetic Factorsmentioning

confidence: 99%

Host genetics and microbiome associations from the lens of genome wide association studies

Weissbrod

Rothschild

Barkan

et al. 2018

Preprint

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Recent studies indicate that the gut microbiome is partially heritable, motivating the need to investigate microbiome-host genome associations via microbial genome-wide association studies (mGWAS). Existing mGWAS demonstrate that microbiome-host genotypes associations are typically weak and are spread across multiple variants, similar to associations often observed in genome-wide association studies (GWAS) of complex traits. Here we reconsider mGWAS by viewing them through the lens of GWAS, and demonstrate that there are striking similarities between the challenges and pitfalls faced by the two study designs. We further advocate the mGWAS community to adopt three key lessons learned over the history of GWAS: (a) Adopting uniform data and reporting formats to facilitate replication and meta-analysis efforts; (b) enforcing stringent statistical criteria to reduce the number of false positive findings; and (c) considering the microbiome and the host genome as distinct entities, rather than studying different taxa and single nucleotide polymorphism (SNPs) separately. Finally, we anticipate that mGWAS sample sizes will have to increase by orders of magnitude to reproducibly associate the host genome with the gut microbiome.PeerJ Preprints | https://doi.org/10.7287/peerj.preprints.26615v1 | CC BY 4.0 Open Access | rec: 5

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Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity

et al. 2021

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IMPORTANCE Previous studies have indicated that gut microbiome may be associated with development of type 2 diabetes. However, these studies are limited by small sample size and insufficient for confounding. Furthermore, which specific taxa play a role in the development of type 2 diabetes remains unclear. OBJECTIVE To examine associations of gut microbiome composition with insulin resistance and type 2 diabetes in a large population-based setting controlling for various sociodemographic and lifestyle factors. DESIGN, SETTING, AND PARTICIPANTS This cross-sectional analysis included 2166 participants from 2 Dutch population-based prospective cohorts: the Rotterdam Study and the LifeLines-DEEP study. EXPOSURES The 16S ribosomal RNA method was used to measure microbiome composition in stool samples collected between January 1, 2012, and December 31, 2013. The α diversity (Shannon, richness, and Inverse Simpson indexes), β diversity (Bray-Curtis dissimilarity matrix), and taxa (from domain to genus level) were identified to reflect gut microbiome composition. MAIN OUTCOMES AND MEASURES Associations among α diversity, β diversity, and taxa with the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and with type 2 diabetes were examined. Glucose and insulin were measured to calculate the HOMA-IR. Type 2 diabetes cases were identified based on glucose levels and medical records from January 2012 to December 2013. Analyses were adjusted for technical covariates, lifestyle, sociodemographic, and medical factors.

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Population-level analysis of gut microbiome variation

Cited by 1,889 publications

References 54 publications

Effect of water flow and chemical environment on microbiota growth and composition in the human colon

Effect of water flow and chemical environment on microbiota growth and composition in the human colon

Host genetics and microbiome associations from the lens of genome wide association studies

Association of Insulin Resistance and Type 2 Diabetes With Gut Microbial Diversity

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